- Developers & Engineers: Deploy reasoning-capable LLMs without MoE complexity—ideal for edge and server deployment.
- Researchers & Students: Study distilled reasoning behavior in smaller models with full transparency.
- Enterprises & Startups: Benefit from top-tier benchmark performance with reduced compute footprints.
- Open-Source Advocates: Use, modify, and redistribute models freely under MIT license.
- Benchmarkers: Compare distilled versions (1.5B–70B) with dense peers like Qwen and Llama in reasoning and code tasks.
How to Use DeepSeek R1 Distill?
- Choose a Variant: Options include Qwen-1.5B, 7B, 14B, 32B, and Llama-8B/70B distilled checkpoints.
- Load Locally or on Cloud: Install via Hugging Face or run with tools like Ollama or vLLM.
- Provide Prompts: Use up to 32K tokens; models support chain-of-thought reasoning in math, coding, QA.
- Run Inference: Serve via local or cloud inference frameworks, or deploy as Chatbots or API services.
- Optimize & Expand: Best scaling seen in Qwen-32B variant; custom fine-tuning possible.
Qwen‑32B leads dense model benchmarks across tasks
Fully open-source—great for innovation and transparency
Versatile sizes for diverse deployment targets
Easy deployment via Hugging Face, Ollama, vLLM
- - *High Benchmark Efficiency*: Distilled variants, especially Qwen-32B, rival or surpass o1-mini on math, code, and reasoning tests.
- - *Compact Reasoning Models*: Bring chain-of-thought from massive MoE models into manageable dense models.
- - *Open-Source & MIT Licensed*: Enables commercial and academic use, fine-tuning, redistribution.
- - *Accessibility*: Usable on commodity hardware with inference tools like Ollama, vLLM.
- - *Broad Range & Flexibility*: Available in sizes from 1.5B to 70B to fit different latency/accuracy needs.
- Still lacks MoE-level context window and multimodal input
- Slightly behind full DeepSeek R1 in reasoning depth
- Early versions may show benchmark volatility depending on evaluation setup
Custom
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Editorial Note
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